Search Results (2,943)

Microcystin-leucine arginine (MC-LR) poses a serious threat to aquatic animals during cyanobacterial blooms. Recently, biochar (BC), derived from rice straw, has emerged as a potent adsorbent for eliminating hazardous contaminants from water. To assess the joint hepatotoxic effects of environmentally relevant concentrations of MC-LR and BC on fish, male adult zebrafish (Danio rerio) were sub-chronically co-exposed to varying concentrations of MC-LR (0, 1, 5, and 25 μg/L) and BC (0 and 100 μg/L) in a fully factorial experiment. After 30 days exposure, our findings suggested that the existence of BC significantly decreased MC-LR bioavailability in liver. Furthermore, histopathological analysis revealed that BC mitigated MC-LR-induced hepatic lesions, which were characterized by mild damage, such as vacuolization, pyknotic nuclei, and swollen mitochondria. Compared to the groups exposed solely to MC-LR, decreased malondialdehyde (MDA) and increased catalase (CAT) and superoxide dismutase (SOD) were noticed in the mixture groups. Concurrently, significant changes in the mRNA expression levels of Nrf2 pathway genes (cat, sod1, gstr, keap1a, nrf2a, and gclc) further proved that BC reduces the oxidative damage induced by MC-LR. These findings demonstrate that BC decreases MC-LR bioavailability in the liver, thereby alleviating MC-LR-induced hepatotoxicity through the Nrf2 signaling pathway in zebrafish. Our results also imply that BC could serve as a potentially environmentally friendly material for mitigating the detrimental effects of MC-LR on fish. Full article

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets—FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models—BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set. Full article

This study investigates the biomechanical differences between typically developed (TD) individuals and those with contralateral hemiplegia (CH) using musculoskeletal modeling in OpenSim. Ten TD participants and ten CH patients were analyzed for joint angles and external joint moments around the three anatomical axes: frontal, sagittal, and transverse. The analysis focused on hip, pelvis, lumbar, knee, ankle, and subtalar joint movements, leveraging MRI-derived bone length data and gait analysis. Significant differences (p < 0.05) were observed in hip flexion, pelvis tilt, lumbar extension, and ankle joint angles, highlighting the impact of hemiplegia on these specific joints. However, parameters like hip adduction and rotation, knee moment, and subtalar joint dynamics did not show significant differences, with p > 0.05. The comparison of joint angle and joint moment correlations between TD and CH participants highlights diverse coordination patterns in CH. Joint angles show significant shifts, such as HF and LR (−0.35 to −0.97) and PR and LR (0.22 to −0.78), reflecting disrupted interactions, while others like HR and LR (0.42 to 0.75) exhibit stronger coupling in CH individuals. Joint moments remain mostly stable, with HF and HA (0.54 to 0.53) and PR and LR (−0.51 to −0.50) showing negligible changes. However, some moments, like KA and HF (0.11 to −0.13) and PT and KA (0.75 to 0.67), reveal weakened or altered relationships. These findings underscore biomechanical adaptations and compensatory strategies in CH patients, affecting joint coordination. Overall, CH individuals exhibit stronger negative correlations, reflecting impaired coordination. These findings provide insight into the musculoskeletal alterations in hemiplegic patients, potentially guiding the development of targeted rehabilitation strategies. Full article

Background/Objectives: Balanites aegyptiaca Del. (Zygophyllaceae) is widely used in traditional medicine, both human and veterinary, throughout Africa for its many properties, including antiparasitic properties. This experimental study aims to optimize the extraction conditions of the seeds of Balanites aegyptiaca Del. Methods: Aqueous maceration was carried out with mass-to-volume ratios of 40%, 30%, 20%, 10% and 5% and extraction times of 6, 12, 24, 36 and 48 h. Extraction yields, phytochemical screening, saponins assay, antioxidant activities ABTS+ free radical scavenging activities, Ferric-reducing antioxidant power (FRAP) assay and antiparasitic tests on Heligmosomoides bakeri were used to compare the different extracts. Results: The pharmaco-chemical study generally showed that aqueous maceration gave the best results, with a mass/volume ratio of 10% after 12 h of maceration. The yield obtained was 28.03% with a saponins content of 13.81 mg/g. The antioxidant activities were 4.25 ± 0.17 µg/mL by the ABTS method and 0.739 µg/mL by the FRAP method. The larvicidal activity also showed that the 10% 12 h extract produced 100% larval mortality from 25 µg/mL. Conclusions: These data provide a basis for guiding the extraction process parameters in producing this antiparasitic phytomedicine. Full article

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions. Full article

Background/Objectives: Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). Methods: In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with ${\mathrm{I}\mathrm{C}}_{50}$ values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. Results: A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. Conclusions: This study provides an effective method for screening AISMs, potentially impacting drug discovery and design. Full article

Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop yield and water productivity. To reduce the field damage and increase measurement accuracy under traditional canopy water interception measurement, UAVs equipped with multispectral cameras were used to extract in situ crop canopy information. Based on the correlation coefficient (r), vegetative indices that are sensitive to canopy interception were screened out and then used to develop canopy interception models using linear regression (LR), random forest (RF), and back propagation neural network (BPNN) methods, and lastly these models were evaluated by root mean square error (RMSE) and mean relative error (MRE). Results show the canopy water interception is first closely related to relative normalized difference vegetation index (R_△NDVI) with r of 0.76. The first seven indices with r from high to low are R_△NDVI, reflectance values of the blue band (Blue), reflectance values of the near-infrared band (Nir), three-band gradient difference vegetation index (TGDVI), difference vegetation index (DVI), normalized difference red edge index (NDRE), and soil-adjusted vegetation index (SAVI) were chosen to develop canopy interception models. All the developed linear regression models based on three indices (R_△NDVI, Blue, and NDRE), the RF model, and the BPNN model performed well in canopy water interception estimation (r: 0.53–0.76, RMSE: 0.18–0.27 mm, MRE: 21–27%) when the interception is less than 1.4 mm. The three methods underestimate the canopy interception by 18–32% when interception is higher than 1.4 mm, which could be due to the saturation of NDVI when leaf area index is higher than 4.0. Because linear regression is easy to perform, then the linear regression method with NDVI is recommended for canopy interception estimation of sprinkler-irrigated winter wheat. The proposed linear regression method and the R_△NDVI index can further be used to estimate the canopy water interception of other plants as well as forest canopy. Full article

Reproductive efficiency is a key element of profitability in dairy herds. However, the genetic evaluation of fertility traits is often challenged by the presence of high censorship rates due to various reasons. An easy approach to address this challenge is to remove the censored data from the dataset. However, removing data might bias the genetic evaluation. Therefore, addressing this issue is crucial, particularly for small populations and populations with limited size. This study uses a Moroccan Holstein dataset to compare two Gaussian linear models and a threshold linear model to handle censored records of days open (DO). Data contained 8646 records of days open across the first three parities of 6337 Holstein cows. The pedigree file comprised 11,555 animals and 14.51% of the dataset was censored. The genetic parameters and breeding values of DO were computed using three different methods: a linear model where all censored records were omitted (LM), a penalty method in which a constant equal to one estrus cycle in cattle was added to the maximum value of DO in each contemporary group to impute the censored records (PLM), and a bivariate threshold model with a penalty (PTM). The heritability estimates were equal to 0.021 ± 0.01 (PLM), 0.029 ± 0.01 (LM), and 0.033 ± 0.01 (PTM). The penalty method and the threshold linear model with a penalty showed better prediction accuracy calculated using the LR method (0.21, and 0.20, respectively). PLM and PTM had a high Spearman correlation (0.99) between the estimated breeding values of the validation dataset, which explains the high percentage of common animals in the top 20% of selected animals. The lack of changes in the ranking of animals between PLM and PTM suggests that both methods can be used to address censored data in this population. Full article

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing. Full article

Background: Traditional imaging techniques have limited efficacy in detecting occult cervical lymph node (LN) metastases in head and neck squamous cell carcinoma (HNSCC). Positron emission tomography/computed tomography (PET-CT) has demonstrated potential for assessing HNSCC, but the literature on its efficacy for detecting cervical LN metastases is scarce and exhibits varied outcomes, hindering comparisons. Aim: To compare the efficacy of CT, MRI, PET-CT, and US for detecting LN metastasis in HNSCC with clinically negative neck lymph nodes. Methods: A systematic search was performed using Web of Science, PubMed, Scopus, Embase, and Cochrane databases. Studies comparing CT, MRI, PET-CT, or US to detect cervical metastases in HNSCC were identified. The quality of the studies was assessed using the QUADAS-2 instrument. The positive likelihood ratios (+LR) and negative likelihood ratios (−LR), sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR), with 95% confidence intervals (C.I.), were calculated. Analysis was stratified according to lymph node and patient basis. Results: Fifty-seven studies yielded 3791 patients. At the patient level, PET-CT exhibited the highest diagnostic performance, with a SEN of 74.5% (95% C.I.: 65.4–81.8%) and SPE of 83.6% (95% C.I.: 77.2–88.5%). PET-CT also demonstrated the highest +LR of 4.303 (95% C.I.: 3.082–6.008) and the lowest −LR of 0.249 (95% C.I.: 0.168–0.370), resulting in the highest DOR of 15.487 (95% C.I.: 8.973–26.730). In the evaluation of diagnostic parameters for various imaging modalities on node-based analysis results, MRI exhibited the highest SEN at 77.4%, and PET demonstrated the highest SPE at 96.6% (95% C.I.: 94.4–98%). PET-CT achieved the highest DOR at 24.353 (95% C.I.: 10.949–54.166). Conclusions: PET-CT outperformed other imaging modalities across the majority of studied metrics concerning LN metastasis detection in HNSCC. Full article

Fluorescence imaging has emerged as a powerful tool for detecting surface damage in fruits, yet its application to vegetables such as green bell peppers remains underexplored. This study investigates the fluorescent characteristics of minor mechanical damage, specifically 5 × 5 mm cuts in the exocarp of green bell peppers, which conventional digital imaging techniques fail to classify accurately. Chlorophyll fluorescence imaging was combined with machine learning algorithms—including logistic regression (LR), artificial neural networks (ANN), random forests (RF), k-nearest neighbors (kNN), and the support vector machine (SVM) to classify damaged and sound fruit. The machine learning models demonstrated a high classification accuracy, with calibration and prediction accuracies exceeding 0.86 and 0.96, respectively, across all algorithms. These results underscore the potential of fluorescence imaging as a non-invasive, rapid, and cheaper method for assessing mechanical damage in green bell peppers, offering valuable applications in quality control and postharvest management. Full article

Background: Migraine diagnosis is mainly clinically based on symptomatology. The objectives of this study were (1) to determine the ability of pain thresholds to differentiate between women with and without migraines and (2) to determine the ability of clinical, psychological and psychophysical variables to differentiate between women with episodic and chronic migraines. A diagnostic accuracy study was conducted. Methods: Pressure-pain thresholds (PPTs) at one trigeminal (temporalis muscle) and one extra-trigeminal (cervical spine) and two distant-pain free (second metacarpal and tibialis anterior muscle) areas, as well as dynamic pain thresholds (DPTs), were bilaterally assessed in 100 women with migraines, recruited from tertiary hospitals (50% episodic, 50% chronic), and 50 comparable women without headaches. Migraine pain features (headache diary), migraine-associated burden (HDI), anxiety and depressive levels (HADS) and state (STAI-S)–trait (STAI-T) anxiety were also evaluated. The area under the receiver operating characteristic (ROC) curve, with optimal cut-off points, as well as the sensitivity, specificity and positive/negative likelihood ratios (LR) for each variable, were calculated. The women with migraines showed lower PPTs and DPTs than those without migraines. Results: The women with chronic migraines showed lower PPTs in the temporalis muscle than the women with episodic migraines. No clinical, psychological or psychophysical variables exhibited acceptable ROC values (≥0.7) for differentiating between women with and without migraines or between women with episodic and chronic migraines. Conclusions: Although the women with migraines had widespread pressure-pain hyperalgesia, neither the clinical, psychological nor psychophysical (pain threshold) variable exhibited the proper diagnostic accuracy to distinguish between women with and without migraines or between women with episodic and chronic migraines. New studies should clarify the clinical relevance of the findings of the current study. Full article

Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 weeks) at 36 months corrected age, using clinical predictors. Methods: A retrospective cohort of very preterm infants (23^0–30⁶ weeks) born between January 2004 and December 2016 in Nova Scotia, Canada. Survivors with neurodevelopmental assessment at 36 months corrected age were included. The study sample was randomly split (80:20) into a development and testing datasets. We compared four methods: LR, elastic net (EN), random forest ensemble (RF) and gradient boosting (XGB), in relation to discrimination (AUC), calibration, and diagnostic properties. Results: Of 811 eligible infants, 663 were included (mean gestational age 28 weeks, mean birth weight 1137 g and 52% male). Of those, 195 (29%) developed NDI and 468 (71%) did not. On internal validation using the testing dataset, all four models provided good discrimination of NDI with comparable AUC. RF was superior to the other three methods with a higher AUC (0.79 vs. 0.74, 0.74, and 0.73 for XGB, EN and LR, respectively), but all models have overlapped CIs. Conclusions: In this population-based cohort of very preterm infants, RF was superior to conventional LR in prediction of NDI at 3 years corrected age. Accurate prediction of preterm infants at risk of NDI enables early referrals for intervention programs and resources allocation toward those who are most likely to benefit. Full article

In the domain of cybersecurity, cyber threats targeting network devices are very crucial. Because of the exponential growth of wireless devices, such as smartphones and portable devices, cyber risks are becoming increasingly frequent and common with the emergence of new types of threats. This makes the automatic and accurate detection of network-based intrusion very essential. In this work, we propose a network-based intrusion detection system utilizing the comprehensive feature engineering approach combined with boosting machine-learning (ML) models. A TCP/IP-based dataset with 25,192 data samples from different protocols has been utilized in our work. To improve the dataset, we used preprocessing methods such as label encoding, correlation analysis, custom label encoding, and iterative label encoding. To improve the model’s accuracy for prediction, we then used a unique feature engineering methodology that included novel feature scaling and random forest-based feature selection techniques. We used three conventional models (NB, LR, and SVC) and four boosting classifiers (CatBoostGBM, LightGBM, HistGradientBoosting, and XGBoost) for classification. The 10-fold cross-validation methods were employed to train each model. After an assessment using numerous metrics, the best-performing model emerged as XGBoost. With mean metric values of 99.54 ± 0.0007 for accuracy, 99.53 ± 0.0013 for precision, 99.54 ± 0.001 for recall, and an F1-score of 99.53 ± 0.0014, the XGBoost model produced the best performance overall. Additionally, we showed the ROC curve for evaluating the model, which demonstrated that all boosting classifiers obtained a perfect AUC value of one. Our suggested methodologies show effectiveness and accuracy in detecting network intrusions, setting the stage for the model to be used in real time. Our method provides a strong defensive measure against malicious intrusions into network infrastructures while cyber threats keep varying. Full article

With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and pattern recognition ability of machine learning models to analyze the relevant information of graduate applicants. This study explores three different models—backpropagation neural networks (BPNN), random forests (RF), and logistic regression (LR)—and combines them with the firefly algorithm (FA). Through data selection, the model was constructed and verified. By comparing the verification results of the three composite models, the model whose evaluation results were closest to the actual data was selected as the research result. The experimental results show that the evaluation result of the BPNN-FA model is the best, with an R value of 0.8842 and the highest prediction accuracy. At the same time, the influence of each characteristic parameter on the prediction result was analyzed. The results show that CGPA has the greatest influence on the evaluation results, which provides the evaluation direction and evaluation results for the evaluators to analyze the level of students’ scientific research ability, as well as providing impetus to continue to promote the combination of education and artificial intelligence. Full article